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source: trunk/sources/HeuristicLab.Modeling/3.2/DefaultClassificationOperators.cs @ 2705

Last change on this file since 2705 was 2388, checked in by gkronber, 15 years ago

Adapted ModelingResultCalculators to keep a mapping from ModelingResult to evaluators. Using mapping in DefaultOperators for data-modeling engines. #755

File size: 3.2 KB
RevLine 
[2340]1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2008 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using HeuristicLab.Core;
23using HeuristicLab.DataAnalysis;
24using HeuristicLab.Operators;
25using HeuristicLab.Modeling;
26using HeuristicLab.Data;
27
[2356]28namespace HeuristicLab.Modeling {
29  public static class DefaultClassificationOperators {
[2353]30    public static IOperator CreatePostProcessingOperator() {
[2356]31      CombinedOperator op = new CombinedOperator();
32      op.Name = "Classification model analyzer";
33
[2340]34      SequentialProcessor seq = new SequentialProcessor();
[2388]35      seq.AddSubOperator(DefaultModelAnalyzerOperators.CreatePostProcessingOperator(ModelType.Classification));
[2340]36
[2356]37      SimpleConfusionMatrixEvaluator trainingConfusionMatrixEvaluator = new SimpleConfusionMatrixEvaluator();
38      trainingConfusionMatrixEvaluator.Name = "TrainingConfusionMatrixEvaluator";
39      trainingConfusionMatrixEvaluator.GetVariableInfo("Values").ActualName = "TrainingValues";
40      trainingConfusionMatrixEvaluator.GetVariableInfo("ConfusionMatrix").ActualName = "TrainingConfusionMatrix";
41      SimpleConfusionMatrixEvaluator validationConfusionMatrixEvaluator = new SimpleConfusionMatrixEvaluator();
42      validationConfusionMatrixEvaluator.Name = "ValidationConfusionMatrixEvaluator";
43      validationConfusionMatrixEvaluator.GetVariableInfo("Values").ActualName = "ValidationValues";
44      validationConfusionMatrixEvaluator.GetVariableInfo("ConfusionMatrix").ActualName = "ValidationConfusionMatrix";
45      SimpleConfusionMatrixEvaluator testConfusionMatrixEvaluator = new SimpleConfusionMatrixEvaluator();
46      testConfusionMatrixEvaluator.Name = "TestConfusionMatrixEvaluator";
47      testConfusionMatrixEvaluator.GetVariableInfo("Values").ActualName = "TestValues";
48      testConfusionMatrixEvaluator.GetVariableInfo("ConfusionMatrix").ActualName = "TestConfusionMatrix";
[2340]49
[2356]50      seq.AddSubOperator(trainingConfusionMatrixEvaluator);
51      seq.AddSubOperator(validationConfusionMatrixEvaluator);
52      seq.AddSubOperator(testConfusionMatrixEvaluator);
53
[2340]54      op.OperatorGraph.AddOperator(seq);
55      op.OperatorGraph.InitialOperator = seq;
[2356]56
[2340]57      return op;
58    }
[2344]59
[2356]60    public static IOperator CreateProblemInjector() {
61      return DefaultRegressionOperators.CreateProblemInjector();
[2344]62    }
[2356]63
64    public static IAnalyzerModel PopulateAnalyzerModel(IScope modelScope, IAnalyzerModel model) {
[2388]65      return DefaultModelAnalyzerOperators.PopulateAnalyzerModel(modelScope, model, ModelType.Classification);
[2356]66    }
[2340]67  }
68}
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